@cyanheads/pubmed-mcp-server
Version:
Production-ready PubMed Model Context Protocol (MCP) server that empowers AI agents and research tools with comprehensive access to PubMed's article database. Enables advanced, automated LLM workflows for searching, retrieving, analyzing, and visualizing
150 lines • 7.41 kB
JavaScript
/**
* @fileoverview Defines the Zod input schema and TypeScript types for the pubmed_research_agent tool.
* This schema accepts detailed components of a research plan from the client,
* which the tool will then structure into a standardized output format.
* @module pubmedResearchAgent/logic/inputSchema
*/
import { z } from "zod";
export const PubMedResearchAgentInputSchema = z.object({
// Overall Project Information
project_title_suggestion: z
.string()
.min(5)
.describe("A concise and descriptive title for the research project."),
primary_research_goal: z
.string()
.min(10)
.describe('The main scientific objective or central question the research aims to address (e.g., "To investigate the role of TREM2 in microglial response to amyloid-beta plaques").'),
research_keywords: z
.array(z.string().min(1))
.min(1)
.describe('Core scientific keywords or MeSH terms defining the research domain (e.g., ["neuroinflammation", "Alzheimer\'s disease", "TREM2"]).'),
organism_focus: z
.string()
.optional()
.describe('Primary organism(s) or model systems (e.g., "Homo sapiens (iPSC-derived microglia)", "Mus musculus (5xFAD model)").'),
// Phase 1: Conception and Planning Inputs
p1_introduction_and_background: z
.string()
.optional()
.describe("Brief overview of the research area, its significance, and relevant background information leading to this study."),
p1_specific_research_question: z
.string()
.optional()
.describe("The precise, focused primary research question the study will answer."),
p1_knowledge_gap: z
.string()
.optional()
.describe("Statement clearly identifying the specific gap in current knowledge this research addresses."),
p1_primary_hypothesis: z
.string()
.optional()
.describe("The main, testable hypothesis. Should be clear, specific, and falsifiable."),
p1_secondary_questions_or_hypotheses: z
.array(z.string())
.optional()
.describe("Any secondary questions or hypotheses to be explored."),
p1_pubmed_search_strategy_description: z
.string()
.optional()
.describe("Description of the primary literature search strategy (e.g., for PubMed), including key terms and database considerations."),
p1_literature_review_scope: z
.string()
.optional()
.describe("The defined scope for the literature review (e.g., timeframes, study types, key themes)."),
p1_lit_review_databases_and_approach: z
.string()
.optional()
.describe("Key databases (e.g., PubMed, EMBASE) and the search approach (e.g., iterative queries, snowballing)."),
p1_experimental_paradigm: z
.string()
.optional()
.describe("The overarching experimental design or study type (e.g., 'comparative multi-omics analysis', 'longitudinal cohort study')."),
p1_data_acquisition_plan_existing_data: z
.string()
.optional()
.describe("Strategy for identifying and retrieving relevant existing datasets (databases, data types, tools)."),
p1_data_acquisition_plan_new_data: z
.string()
.optional()
.describe("Plan for generating novel data (data types, experimental models, key procedures, deposition plan)."),
p1_blast_utilization_plan: z
.string()
.optional()
.describe("If applicable, how sequence alignment services (e.g., NCBI BLAST) will be used (purpose, programs, databases)."),
p1_controls_and_rigor: z
.string()
.optional()
.describe("Description of key experimental controls and measures to ensure scientific rigor and reproducibility."),
p1_methodological_challenges_and_mitigation: z
.string()
.optional()
.describe("Anticipated methodological challenges and proposed mitigation strategies."),
// Phase 2: Data Collection and Processing Inputs
p2_data_collection_methods_wet_lab: z
.string()
.optional()
.describe("Specific wet-lab protocols if new data is generated (sample prep, treatments, instruments)."),
p2_data_collection_methods_dry_lab: z
.string()
.optional()
.describe("Execution details for data retrieval from databases (queries, tools, accessioning)."),
p2_data_preprocessing_and_qc_plan: z
.string()
.optional()
.describe("Pipeline for data cleaning, preprocessing (e.g., alignment, normalization), and quality control (metrics, thresholds, tools)."),
// Phase 3: Analysis and Interpretation Inputs
p3_data_analysis_strategy: z
.string()
.optional()
.describe("Core statistical and computational methods to analyze data and test hypotheses (tests, software, ML models if any)."),
p3_bioinformatics_pipeline_summary: z
.string()
.optional()
.describe("Summary of the bioinformatics pipeline for high-throughput data analysis (tools, downstream analyses)."),
p3_results_interpretation_framework: z
.string()
.optional()
.describe("Framework for evaluating findings against hypotheses (statistical significance, biological relevance)."),
p3_comparison_with_literature_plan: z
.string()
.optional()
.describe("Strategy for contextualizing results with existing literature and addressing discrepancies."),
// Phase 4: Dissemination and Iteration Inputs
p4_dissemination_manuscript_plan: z
.string()
.optional()
.describe("Plan for manuscript preparation (core message, target journal profile, key figures)."),
p4_dissemination_data_deposition_plan: z
.string()
.optional()
.describe("Strategy for depositing data in public repositories (types, repositories, FAIR principles)."),
p4_peer_review_and_publication_approach: z
.string()
.optional()
.describe("Approach to journal submission and addressing peer review."),
p4_future_research_directions: z
.string()
.optional()
.describe("Potential next steps, new questions, or translational applications arising from the research."),
// Cross-Cutting Considerations Inputs
cc_record_keeping_and_data_management: z
.string()
.optional()
.describe("Plan for record-keeping, version control, data storage, and DMP."),
cc_collaboration_strategy: z
.string()
.optional()
.describe("If applicable, strategy for collaboration, communication, roles, and authorship."),
cc_ethical_considerations: z
.string()
.optional()
.describe("Ethical considerations, IRB/IACUC approval plans, data privacy, RCR training."),
// Meta-parameter from previous iterations, still useful
include_detailed_prompts_for_agent: z
.boolean()
.optional()
.default(false) // Default to false, meaning the tool primarily structures the detailed input.
.describe("If true, the tool will add more detailed instructive prompts/guidance within the output fields for a research agent. If false (default), it will primarily structure the provided inputs with minimal additional prompting."),
});
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